#valores de los 6 parametros
##log_ro,steepness,log.m,log_avgrec,prho,varphi
ival <- c(6,0.8,-0.3566749,6,0.3,0.75)
#lower bound of parameters
lb <- c(5,0.2,-5,5,0.001,0.01)
#upper bound of parameters
ub <- c(15,1,5,15,0.999,500)
#phase de estimacion
phz <- c(4,5,-5,1,3,3)
#prior type for parameters
# 0: uniform (0,0)
# 1: normal (p1=mu,p2=sig)
# 2: lognormal (p1=log(mu),p2=sig)
# 3: beta (p1=alpha,p2=beta)
# 4: gamma (p1=alpha,p2=beta)
prior <- c(0,3,2,2,3,4)
#parameters p1 and p2
p1 <- c(5 ,10,-0.3566749,5, 5,150)  
p2 <- c(15,5,0.10000000,15,5,125)
npar <- 6
theta <- cbind(ival,lb,ub,phz,prior,p1,p2)
#Numero de artes de pesca con datos de composicion por tallas
#gear: 1: pesqueria, 2: reclas, 3: pelaces
ngear <- 3
#Tipo de Selectividad para cada arte con datos
# de composicion por edad/tallas
# 1: selectividad logistica con parametros a ser estimados
# 2: selectividad logistica fija sin estimar
#gear: 1: pesqueria, 2: reclas, 3: pelaces
isel_type<-c(2,1,1)
#age al 50% por arte en meses como fraccion de años
# e.g. 6 meses = 6/12
ahat <- c(0.5,0.5,0.5)
#std para la selectividad
ghat <-c(0.2,0.2,0.2)
#phase de estimacion para la selectividad
sel_phz <-c(-3,3,3)
#Controles para los prior de capturabilidad
#Numero de indices de abundancia
#cpue, reclas, pelaces, mph
nints <- 4
#tipo de prior usado para qs
# 1: uniform, 2: normal (log(mu))
q_prior <- c(2,2,2,2)
#valor de qs - en escala log
q_mu <-c(-8,-0.8,0,-2)
#sdt de qs
q_sd <- c(0.1,0.1,0.1,0.2)
#phases q
q_phz <- c(4,3,-3,4)
#CVsurveys
cv_surveys<-c(0.3,0.2,0.2,0.4)
#num for multinomial
nsizemult<-c(150,60,80)
#Verbose
#1 -> verbose
#2 -> recruitment model (1=beverton-holt, 2=rickers)
#3 -> autocorrelation in recruitment
#4 -> std in catch first phase
#5 -> std in catch in last phase
#6 -> mean fishing mortality rate to regularize the solution
#7 -> standard deviation of mean F penalty in first phases
#8 -> standard deviation of mean F penalty in last phase.
cntrl <- c(0,2,0,0.3,0.2,0.1,0.01,4)
#fin
enfc<-999
